1. Field of the Invention
The present invention relates to a method for extracting texture features from a multichannel image, and here particularly to a method for extracting color texture features for automatic classification and segmentation.
2. Description of the Related Art
Recently, more and more color cameras are used in industrial and medical image processing for generating/capturing the images to be examined.
Typical medical applications of such color cameras are, e.g. classification and segmentation of skin melanomas, classification of dysplastic and cancerous cells, e.g. cervix or sputum cells, in microscopy, or the differentiation of healthy, dysplastic or diseased tissue or mucous membrane in endoscopic examinations of oral cavity, throat, larynx, oesophagus, stomach and intestines. Examples for an industrial application of such cameras are classification and examination of wood trunks based on their bark or the cut surface, automatic identification and separation of garbage types on a conveyor belt or support in cartography by satellite pictures.
In the different named fields of application, such colored images captured by color cameras can serve as a base for classification as well as segmentation of the image content. Depending on the application case and specific boundary conditions, either viewing of the whole image or merely one or several image portions, the so-called region of interest (ROI) is performed. In this context, classification means the assignment of one or several object represented in the image to a specific class. In this context, however, segmentation is seen as the determination of such objects by useful integration of individual pixels to larger (object) units, which means a classification and assignment, respectively, of pixels to so-called object classes.
So-called features, which can be calculated from color and the gray value intensities, respectively, of pixels or small groups of pixels serve as a base for such an object and pixel classification, respectively. The calculation of such features from gray level images is thereby based on a skilful integration of the gray levels of the pixels of the viewed image portion. Examples of such simple features are, for example, first order statistics, such as frequency of gray levels (histograms), mean values and variances of the viewed gray levels in such an image portion. More complex features, by which, for example, so-called textures and textured surfaces, respectively, can be described and analyzed, are based, e.g. on higher order statistics, so-called Fourier or Gabor features, run length encodings, geometrical statistical features and the like. Examples of such higher order statistics for describing textured surfaces are, for example, described by R. M. Haralick, et al. in “Texture Features for Image Classification”, IEEE Transactions on Systems, Man, and Cybernetics, Vol. SMC-3, No. 6, pages 610–621, November 1973, by M. Unser in “Sum and Difference Histograms for Texture Analysis”, IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol. 8, No. 1, pages 118–125, 1986 and by Y. Q. Chen et al. in “Statistical Geometrical Features for Texture Classification”, Pattern Recognition, Vol. 28, No. 4, pages 537–552, September 1995.
The disadvantage of these known solutions is that they are limited to the evaluation of intensities gathered from a single channel recording of the image and thus merely enable the determination of texture features in such images. Based on the texture features acquired that way, a classification can conventionally be performed.
In contrast to gray level images, however, it has been determined that color images and particularly information with regard to the statistical distribution of colors are a very powerful and useful classification tool, so that utilization of color information contained in a color image is desirable.
In the prior art, several solutions are known by which the above-mentioned solutions for extracting texture features from gray level images can be converted to multichannel images, such as spectral images with two or more levels. These solutions work such that the analysis steps known from the solutions with regard to the examination of gray level images are applied separately to every channel of the image, which means every image level, and that the resulting texture features are finally integrated to one overall texture feature.
This solution is disadvantageous in that every image level is examined individually, so that the information contained in the actual distribution of the colors of a pixel across the levels are not used for texture feature analysis, but that merely a combination of pixel information contained in all colored channels is performed by a final integration of the separately generated texture features. Thus, no actual combining of the information associated to the individual pixels in the different levels takes place, so that this solution has only slight improvements compared to the conventional “gray level approach”, which, however, do not justify the increased computing effort.